NUBOT: Embedded Knowledge Graph With RASA Framework for Generating Semantic Intents Responses in Roman Urdu
Johar Shabbir, Muhammad Umair Arshad, Waseem Shahzad

TL;DR
This paper presents a method to generate accurate intents from Roman Urdu data and integrates a knowledge graph with the RASA framework to improve chatbot understanding and response accuracy.
Contribution
It introduces embedding a knowledge graph into RASA for semantic intent recognition in Roman Urdu, reducing reliance on extensive labeled datasets.
Findings
Minimum intent accuracy of 64% confidence
Response generation accuracy of 82.1%
Maximum accuracy gain of 96.7%
Abstract
The understanding of the human language is quantified by identifying intents and entities. Even though classification methods that rely on labeled information are often used for the comprehension of language understanding, it is incredibly time consuming and tedious process to generate high propensity supervised datasets. In this paper, we present the generation of accurate intents for the corresponding Roman Urdu unstructured data and integrate this corpus in RASA NLU module for intent classification. We embed knowledge graph with RASA Framework to maintain the dialog history for semantic based natural language mechanism for chatbot communication. We compare results of our work with existing linguistic systems combined with semantic technologies. Minimum accuracy of intents generation is 64 percent of confidence and in the response generation part minimum accuracy is 82.1 percent and…
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Speech and dialogue systems
